Detection of copy number variation on wheat

Ricardo H. Ramirez-Gonzalez

28 Sept 2020

Detection of medium and large CNV

Copy number variations (CNVs) are important for functional analysis and gene specialisation
High througput sequencing across a large sets of samples

Normalisation

	Sample_1	Sample_2	Sample_3	Sample_4
chr1:1:100	19	24	12	14
chr2:101:200	25	23	17	18
chr1:201:300	20	27	12	18
chr2:301:400	19	25	16	10
chr2:401:500	17	27	14	25

Normalisation

	Sample_1	Sample_2	Sample_3	Sample_4
chr1:1:100	19	24	12	14
chr2:101:200	25	23	17	18
chr1:201:300	20	27	12	18
chr2:301:400	19	25	16	10
chr2:401:500	17	27	14	25

Initial data

Coverage across regions a region is not uniform

Normalisation

\(x_{i,j}=\frac{WindowCoverage_{i,j}\times10^{9}}{WindowLength_{i}\times{totalReadsSample_{j}}}\)

\(xnorm_{i,j}=\frac{x_{i,j}}{mean(X_{i})}\)

Normalise by sample

\(x_{i,j}=\frac{WindowCoverage_{i,j}\times10^{9}}{WindowLength_{i}\times{totalReadsSample_{j}}}\)

	Sample_1	Sample_2	Sample_3	Sample_4
chr1:1:100	19	24	12	14
chr2:101:200	25	23	17	18
chr1:201:300	20	27	12	18
chr2:301:400	19	25	16	10
chr2:401:500	17	27	14	25
totalReadsSample	100	126	71	85

Normalisation by window

\(xnorm_{i,j}=\frac{x_{i,j}}{mean(X_{i})}\)

	Sample_1	Sample_2	Sample_3	Sample_4	Window Mean
chr1:1:100	1,919,192	1,924,002	1,707,213	1,663,696	1,803,526
chr2:101:200	2,525,253	1,843,835	2,418,552	2,139,037	2,231,669
chr1:201:300	2,020,202	2,164,502	1,707,213	2,139,037	2,007,739
chr2:301:400	1,919,192	2,004,169	2,276,284	1,188,354	1,847,000
chr2:401:500	1,717,172	2,164,502	1,991,748	2,970,885	2,211,077

Normalised coverage

	Sample_1	Sample_2	Sample_3	Sample_4
chr1:1:100	1.06	1.07	0.95	0.92
chr2:101:200	1.13	0.83	1.08	0.96
chr1:201:300	1.01	1.08	0.85	1.07
chr2:301:400	1.04	1.09	1.23	0.64
chr2:401:500	0.78	0.98	0.90	1.34

Normalised coverage

Second normalisation

Remove the windows with \(sd(window) > 0.3\)
Repeat normalisation
Exclude the datapoints without coverage

Watkins collection

823 landraces sequenced >10X coverage
In collaboration with Griffiths group and Agricultural Genomics Institute of Shenzhen (AGIS)
Search for CNV within genes

Standard deviation of lines across samples (QC).

5 out of 823 lines are noisy (\(\sigma > 0.45\)).

line	SD
WATDE0009	0.75
WATDE0039	1.08
WATDE0056	0.51
WATDE0060	0.52
WATDE0090	0.50

Regular vs noisy line

Zoom out to larger region

Outline represents \(1\pm2.5\sigma\)

Zoom out to larger region

Regular line (\(\sigma<0.45\))

Zoom out to larger region

Noisy line (\(\sigma > 0.45\))

Merging continuous CNVs

Stich individual windows with CNVs
Find the extent of a variation
Bridge over noisy windows

Stich CNV candidates

CNV length distributions

There are 43,412,060 CNV events across 823 lines

CNV length distributions (over 200bp)

The minimum size that we have in this dataset is 200bp 22,294,613 (51.36 %) are not singletons

Next steps

Improve stiching algorithm
Use smaller window size (150bp, 100bp)
Find genes/regions more prone to have CNVs
Analyse in detail known CNVs

Deletions in 𝛾-radiation lines

~600 Paragon lines with radiation induced deletions
Identify lines with missing genes to test changes in phenotype
Low sequencing coverage (0.3x)
Large window sizes to make up for low coverage
Collaboration with Nichlson lab.

Normalised coverage in a line and region

Deletions across genome

Paragon deletions website

http://wheat-deletion.cyverseuk.org

Next steps

Find genes without deletions
Find a minimal set of lines that cover all the genes that can be deleted

Acknowledgments

WatSeq: Simon Griffiths (JIC), Cheng Shifeng (AGIS), Burkhard Stauernagel (JIC), CiS
Paragon 𝛾-radiation lines: Paul Nichleson (JIC), Ben Hales (JIC), Anil Thanki (EI), Matt Clark (EI/NHM)